# import libs
import requests
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import os
import tweepy
from tweepy import OAuthHandler
from timeit import default_timer as timer
import json
import seaborn as sns
import time
import matplotlib.patches as mpatches
pd.set_option("display.max_rows", None, "display.max_columns", None, "display.max_colwidth", None)

Gathering Data¶

sources:¶

Twitter-archive-enhanced.csv
Image-predictions.tsv (download it from udacity's server)
Twitter API (using tweet-json.txt)

Twitter-archive-enhanced.csv¶

# Twitter-archive-enhanced.csv
twitter_archive_df = pd.read_csv('twitter-archive-enhanced.csv')

twitter_archive_df.head()

Image-predication (download it from the server)¶

folder_name = 'Image-predictions'
url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'
response = requests.get(url)

# Make directory if it doesn't already exist
if not os.path.exists(folder_name):
    os.makedirs(folder_name)
# Write image prediction file (tsv)
with open(os.path.join(folder_name,url.split("/")[-1]), mode='wb') as file:
    file.write(response.content)

# check if the file download successfully
os.listdir(folder_name)

['image-predictions.tsv']

image_predictions_df = pd.read_csv(folder_name + "/image-predictions.tsv", sep='\t')
image_predictions_df.head()

Twitter API (using tweet-json.txt)¶

# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file
# These are hidden to comply with Twitter's API terms and conditions
consumer_key = 'XXXXXXX'
consumer_secret = 'XXXXXXX'
access_token = 'XXXXXXX'
access_secret = 'XXXXXXX'

auth = OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_secret)

api = tweepy.API(auth, wait_on_rate_limit=True)

# NOTE TO STUDENT WITH MOBILE VERIFICATION ISSUES:
# df_1 is a DataFrame with the twitter_archive_enhanced.csv file. You may have to
# change line 17 to match the name of your DataFrame with twitter_archive_enhanced.csv
# NOTE TO REVIEWER: this student had mobile verification issues so the following
# Twitter API code was sent to this student from a Udacity instructor
# Tweet IDs for which to gather additional data via Twitter's API
#tweet_ids = df_1.tweet_id.values
#len(tweet_ids)

# Query Twitter's API for JSON data for each tweet ID in the Twitter archive
#count = 0
#fails_dict = {}
#start = timer()
## Save each tweet's returned JSON as a new line in a .txt file
#with open('tweet_json.txt', 'w') as outfile:
#    # This loop will likely take 20-30 minutes to run because of Twitter's rate limit
#    for tweet_id in tweet_ids:
#        count += 1
#        print(str(count) + ": " + str(tweet_id))
#        try:
#            tweet = api.get_status(tweet_id, tweet_mode='extended')
#            print("Success")
#            json.dump(tweet._json, outfile)
#            outfile.write('\n')
#        except tweepy.TweepError as e:
#            print("Fail")
#            fails_dict[tweet_id] = e
#            pass
#end = timer()
#print(end - start)
#print(fails_dict)

Read from json file

tweet_df = pd.read_json('tweet-json.txt', lines=True)
tweet_df.head()

Assessing Data¶

twitter_archive_df¶

twitter_archive_df.head()

twitter_archive_df

# info check
twitter_archive_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   tweet_id                    2356 non-null   int64  
 1   in_reply_to_status_id       78 non-null     float64
 2   in_reply_to_user_id         78 non-null     float64
 3   timestamp                   2356 non-null   object 
 4   source                      2356 non-null   object 
 5   text                        2356 non-null   object 
 6   retweeted_status_id         181 non-null    float64
 7   retweeted_status_user_id    181 non-null    float64
 8   retweeted_status_timestamp  181 non-null    object 
 9   expanded_urls               2297 non-null   object 
 10  rating_numerator            2356 non-null   int64  
 11  rating_denominator          2356 non-null   int64  
 12  name                        2356 non-null   object 
 13  doggo                       2356 non-null   object 
 14  floofer                     2356 non-null   object 
 15  pupper                      2356 non-null   object 
 16  puppo                       2356 non-null   object 
dtypes: float64(4), int64(3), object(10)
memory usage: 313.0+ KB

# checking for null value (since many columns showing less value than others)
twitter_archive_df.isnull().sum()

tweet_id                         0
in_reply_to_status_id         2278
in_reply_to_user_id           2278
timestamp                        0
source                           0
text                             0
retweeted_status_id           2175
retweeted_status_user_id      2175
retweeted_status_timestamp    2175
expanded_urls                   59
rating_numerator                 0
rating_denominator               0
name                             0
doggo                            0
floofer                          0
pupper                           0
puppo                            0
dtype: int64

twitter_archive_df.describe()

Quality isssues:¶

timestamp column has +0000. This should be removed
timestamp column has dtpye as object. It should be changed to datetime dtype.
source column shows the (href ..) link. The source should be exracted out of the link.
Dogs name column has some rows with strange names. Name's examples (an, None, 0, a, Al, my, this, all, old, infuriating, the). It will be replaced None.
Original tweets only. Rows with retweets and replay should be removed
These columns (in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp) related to replays and retweets, it will be removed.
Some dogs appeare in more than one stage e.x. (doggo and floofer). These rows will be removed.
Some columns have None value. It should be changed to NaN.
There some decimal dog rating values. We should get it from text

Tidiness¶

twitter_archive_df is should be the main Data frame. Therefore it should be merge with other
doggo, floofer, pupper and puppo should be represented in one column named dog_stage.

image_predictions_df¶

image_predictions_df.head()

image_predictions_df

image_predictions_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2075 entries, 0 to 2074
Data columns (total 12 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   tweet_id  2075 non-null   int64  
 1   jpg_url   2075 non-null   object 
 2   img_num   2075 non-null   int64  
 3   p1        2075 non-null   object 
 4   p1_conf   2075 non-null   float64
 5   p1_dog    2075 non-null   bool   
 6   p2        2075 non-null   object 
 7   p2_conf   2075 non-null   float64
 8   p2_dog    2075 non-null   bool   
 9   p3        2075 non-null   object 
 10  p3_conf   2075 non-null   float64
 11  p3_dog    2075 non-null   bool   
dtypes: bool(3), float64(3), int64(2), object(4)
memory usage: 152.1+ KB

image_predictions_df.isnull().sum()

tweet_id    0
jpg_url     0
img_num     0
p1          0
p1_conf     0
p1_dog      0
p2          0
p2_conf     0
p2_dog      0
p3          0
p3_conf     0
p3_dog      0
dtype: int64

image_predictions_df.describe()

Qaulity issues¶

columns(p1,p2,p3): showing first letter issue (capital and small letter) and underscore (_) need to be replaced with space
p1, p2 and p3 as predictions, New column should be created to go through these predictions and finalize if the dog is breed or not. It will pick the first true predictions

tweet_df¶

tweet_df.head()

tweet_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2354 entries, 0 to 2353
Data columns (total 31 columns):
 #   Column                         Non-Null Count  Dtype              
---  ------                         --------------  -----              
 0   created_at                     2354 non-null   datetime64[ns, UTC]
 1   id                             2354 non-null   int64              
 2   id_str                         2354 non-null   int64              
 3   full_text                      2354 non-null   object             
 4   truncated                      2354 non-null   bool               
 5   display_text_range             2354 non-null   object             
 6   entities                       2354 non-null   object             
 7   extended_entities              2073 non-null   object             
 8   source                         2354 non-null   object             
 9   in_reply_to_status_id          78 non-null     float64            
 10  in_reply_to_status_id_str      78 non-null     float64            
 11  in_reply_to_user_id            78 non-null     float64            
 12  in_reply_to_user_id_str        78 non-null     float64            
 13  in_reply_to_screen_name        78 non-null     object             
 14  user                           2354 non-null   object             
 15  geo                            0 non-null      float64            
 16  coordinates                    0 non-null      float64            
 17  place                          1 non-null      object             
 18  contributors                   0 non-null      float64            
 19  is_quote_status                2354 non-null   bool               
 20  retweet_count                  2354 non-null   int64              
 21  favorite_count                 2354 non-null   int64              
 22  favorited                      2354 non-null   bool               
 23  retweeted                      2354 non-null   bool               
 24  possibly_sensitive             2211 non-null   float64            
 25  possibly_sensitive_appealable  2211 non-null   float64            
 26  lang                           2354 non-null   object             
 27  retweeted_status               179 non-null    object             
 28  quoted_status_id               29 non-null     float64            
 29  quoted_status_id_str           29 non-null     float64            
 30  quoted_status                  28 non-null     object             
dtypes: bool(4), datetime64[ns, UTC](1), float64(11), int64(4), object(11)
memory usage: 505.9+ KB

tweet_df.isnull().sum()

created_at                          0
id                                  0
id_str                              0
full_text                           0
truncated                           0
display_text_range                  0
entities                            0
extended_entities                 281
source                              0
in_reply_to_status_id            2276
in_reply_to_status_id_str        2276
in_reply_to_user_id              2276
in_reply_to_user_id_str          2276
in_reply_to_screen_name          2276
user                                0
geo                              2354
coordinates                      2354
place                            2353
contributors                     2354
is_quote_status                     0
retweet_count                       0
favorite_count                      0
favorited                           0
retweeted                           0
possibly_sensitive                143
possibly_sensitive_appealable     143
lang                                0
retweeted_status                 2175
quoted_status_id                 2325
quoted_status_id_str             2325
quoted_status                    2326
dtype: int64

tweet_df.describe()

Quality issues:¶

(id) This should should be changed to tweet_id
(id, retweet_count and favorite_count) these the only columns that should be kept, other columns will be removed

Cleaning Data¶

Making A copy of each dataframe

twitter_archive_df_clean = twitter_archive_df.copy()
image_predictions_df_clean = image_predictions_df.copy()
tweet_df_clean = tweet_df.copy()

Quality issue¶

Define:¶

twitter_archive_df timestamp column has +0000. This should be removed and the time should be extracted out.

twitter_archive_df_clean.timestamp[0]

'2017-08-01 16:23:56 +0000'

code:¶

twitter_archive_df_clean.timestamp = twitter_archive_df_clean.timestamp.str.extract('(.*) +')

Test:¶

twitter_archive_df_clean.timestamp[:5]

0    2017-08-01 16:23:56
1    2017-08-01 00:17:27
2    2017-07-31 00:18:03
3    2017-07-30 15:58:51
4    2017-07-29 16:00:24
Name: timestamp, dtype: object

Quality issue¶

Define:¶

twitter_archive_df timestamp column has dtpye as object. It should be changed to datetime dtype

code:¶

twitter_archive_df_clean.timestamp = pd.to_datetime(twitter_archive_df_clean.timestamp)

Test¶

twitter_archive_df_clean.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype         
---  ------                      --------------  -----         
 0   tweet_id                    2356 non-null   int64         
 1   in_reply_to_status_id       78 non-null     float64       
 2   in_reply_to_user_id         78 non-null     float64       
 3   timestamp                   2356 non-null   datetime64[ns]
 4   source                      2356 non-null   object        
 5   text                        2356 non-null   object        
 6   retweeted_status_id         181 non-null    float64       
 7   retweeted_status_user_id    181 non-null    float64       
 8   retweeted_status_timestamp  181 non-null    object        
 9   expanded_urls               2297 non-null   object        
 10  rating_numerator            2356 non-null   int64         
 11  rating_denominator          2356 non-null   int64         
 12  name                        2356 non-null   object        
 13  doggo                       2356 non-null   object        
 14  floofer                     2356 non-null   object        
 15  pupper                      2356 non-null   object        
 16  puppo                       2356 non-null   object        
dtypes: datetime64[ns](1), float64(4), int64(3), object(9)
memory usage: 313.0+ KB

Quality issue¶

define¶

There some decimal dog rating values

twitter_archive_df_clean[twitter_archive_df_clean.text.str.contains(r"(\d+\.\d*\/\d+)")][['text', 'rating_numerator']]

code¶

twitter_archive_df_clean.loc[45,'rating_numerator'] = 13.5
twitter_archive_df_clean.loc[695,'rating_numerator'] = 9.75
twitter_archive_df_clean.loc[763, 'rating_numerator'] = 11.27
twitter_archive_df_clean.loc[1712, 'rating_numerator'] = 11.26

Test¶

twitter_archive_df_clean[twitter_archive_df_clean.text.str.contains(r"(\d+\.\d*\/\d+)")][['text', 'rating_numerator']]

/Users/abdulazizalabdullatif/opt/anaconda3/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.
  return func(self, *args, **kwargs)

Quality issue¶

Define:¶

twitter_archive_df source column shows the (href ..) link. The source should be exracted out of the link.

twitter_archive_df_clean.source[:2]

0    <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
1    <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
Name: source, dtype: object

code¶

twitter_archive_df_clean.source = twitter_archive_df_clean.source.str.extract('>(.*)<')

Test¶

twitter_archive_df_clean.source[:2]

0    Twitter for iPhone
1    Twitter for iPhone
Name: source, dtype: object

Quality issue¶

Define:¶

Dogs name column has some rows with strange names. Name's examples (an, None, 0, a, Al, my, this, all, old, infuriating, the). It should be repalced so it will be replaced NaN.

code¶

twitter_archive_df_clean.name.replace(['an','None','0','a','Al','my','this','all','old','infuriating','the'],np.nan, inplace=True)

Test¶

twitter_archive_df_clean.name.value_counts()

Charlie           12
Lucy              11
Oliver            11
Cooper            11
Penny             10
Lola              10
Tucker            10
Winston            9
Bo                 9
Sadie              8
Daisy              7
Toby               7
Buddy              7
Bailey             7
Milo               6
Scout              6
Leo                6
Stanley            6
Rusty              6
Jax                6
Oscar              6
Koda               6
Dave               6
Bella              6
Jack               6
Finn               5
Gus                5
Phil               5
Louis              5
Oakley             5
Chester            5
Bentley            5
Sunny              5
Sammy              5
Larry              5
very               5
George             5
Alfie              5
Cassie             4
Derek              4
Brody              4
Ruby               4
Beau               4
Jeffrey            4
Moose              4
Carl               4
Maggie             4
Archie             4
Gerald             4
Loki               4
Reginald           4
Clark              4
Gary               4
quite              4
Jerry              4
Hank               4
Bruce              4
Luna               4
Shadow             4
Scooter            4
Duke               4
Maximus            4
Bear               4
Dexter             4
Reggie             4
Maddie             4
Chip               4
Winnie             4
just               4
one                4
Clarence           4
Sophie             4
Boomer             4
Riley              4
Sampson            4
Walter             4
Vincent            3
Coco               3
Jimothy            3
Wallace            3
Reese              3
Max                3
Wilson             3
Lorenzo            3
Klevin             3
Arnie              3
Ellie              3
Ted                3
Louie              3
Steven             3
Rory               3
Rosie              3
Mia                3
Paisley            3
Samson             3
Otis               3
Frankie            3
Kyle               3
Colby              3
Nala               3
Lily               3
Wyatt              3
Doug               3
Waffles            3
Sebastian          3
Malcolm            3
Zeke               3
Gizmo              3
Zoey               3
Olive              3
Calvin             3
Peaches            3
Earl               3
Philbert           2
Canela             2
Marley             2
Linda              2
Hercules           2
Dash               2
Tyrone             2
Eli                2
Hobbes             2
Cash               2
Balto              2
Penelope           2
Raymond            2
Maxaroni           2
Olivia             2
Stubert            2
Phineas            2
Kenny              2
Rizzy              2
getting            2
Sam                2
Jiminy             2
Layla              2
Bungalo            2
Doc                2
Kirby              2
Moe                2
Calbert            2
Chompsky           2
Meyer              2
Astrid             2
Kilo               2
Cali               2
Herm               2
Pippa              2
Ozzy               2
Betty              2
Benedict           2
Oliviér            2
Indie              2
Jesse              2
Chipson            2
Levi               2
Lou                2
Chelsea            2
Cupcake            2
Fred               2
Davey              2
Hammond            2
Herschel           2
Bisquick           2
Harper             2
Theodore           2
Rufus              2
Roosevelt          2
Rubio              2
Dawn               2
Carly              2
Griffin            2
Butter             2
Belle              2
Sierra             2
Romeo              2
Mister             2
Bell               2
Curtis             2
mad                2
Pickles            2
Juno               2
Albus              2
Mattie             2
Patrick            2
Phred              2
Churlie            2
Ash                2
Gidget             2
Finley             2
Leela              2
Alice              2
Kreg               2
Sansa              2
Elliot             2
Charles            2
Fiona              2
Luca               2
Harold             2
Percy              2
Wally              2
Frank              2
Quinn              2
Lennon             2
Django             2
Titan              2
Atticus            2
Hurley             2
Thumas             2
Atlas              2
Solomon            2
Gabe               2
Ava                2
Piper              2
Blitz              2
Oshie              2
Gromit             2
Kenneth            2
Misty              2
Remington          2
Jamesy             2
Nelly              2
Sarge              2
Jeph               2
Sugar              2
Benji              2
Terry              2
Lincoln            2
Bernie             2
Bubbles            2
Rocky              2
Yogi               2
Chet               2
actually           2
Emmy               2
Timison            2
Kevin              2
Crystal            2
Smokey             2
Opal               2
Tyr                2
Chuckles           2
Shaggy             2
Keurig             2
Eve                2
Coops              2
Franklin           2
Bob                2
Jimison            2
Albert             2
Rocco              2
Odie               2
Baloo              2
Seamus             2
Watson             2
Dakota             2
Abby               2
Baxter             2
Fizz               2
Sandy              2
Ollie              2
Aspen              2
Paull              2
Neptune            2
Lenny              2
Nollie             2
Logan              2
Herald             2
Cody               2
Anakin             2
Klein              2
Ken                2
Brad               2
Moreton            2
Axel               2
Trooper            2
Happy              2
Flávio             2
Kyro               2
Lilly              2
Hunter             2
Panda              2
Terrance           2
CeCe               2
Keith              2
Stephan            2
Pipsy              2
Jackson            2
Kreggory           2
Pablo              2
not                2
Skittle            1
Tilly              1
Alexander          1
Hector             1
Tiger              1
Tedrick            1
Mo                 1
Jaspers            1
Juckson            1
Sparky             1
Mabel              1
Cheesy             1
Nimbus             1
Bluebert           1
Tom                1
Sailor             1
Eevee              1
Iggy               1
Bode               1
Emma               1
Schnitzel          1
Sephie             1
Cora               1
Timofy             1
Dook               1
Darby              1
Jennifur           1
Cermet             1
Bubba              1
Monster            1
Coleman            1
Akumi              1
Angel              1
Jeb                1
unacceptable       1
Carbon             1
Shadoe             1
Pluto              1
Clyde              1
Antony             1
Chubbs             1
Cheryl             1
Arya               1
Timber             1
Hubertson          1
Jarod              1
Rumble             1
Sky                1
Meera              1
Brutus             1
Rizzo              1
Link               1
Mya                1
Kaiya              1
Traviss            1
Rhino              1
Remy               1
Lilah              1
Dobby              1
Cal                1
Severus            1
Laela              1
Marvin             1
Dante              1
Oddie              1
Lupe               1
Dale               1
Wafer              1
Mike               1
Dallas             1
Tuco               1
Halo               1
Kobe               1
Gilbert            1
Jett               1
Stefan             1
such               1
life               1
Sage               1
Brooks             1
William            1
Edgar              1
DonDon             1
Steve              1
Alejandro          1
Bruno              1
Gòrdón             1
Ace                1
Zuzu               1
Tug                1
Major              1
Batdog             1
Snoop              1
Maxwell            1
Franq              1
Lorelei            1
Frönq              1
Schnozz            1
Clifford           1
Ember              1
Heinrich           1
Kendall            1
Karl               1
Shiloh             1
Spencer            1
Chuq               1
Bones              1
Tuck               1
Wesley             1
Jareld             1
Snoopy             1
Edd                1
Richie             1
Bronte             1
Mason              1
Raphael            1
Danny              1
Scott              1
Chef               1
Glacier            1
Chesney            1
Spanky             1
Clarkus            1
Fwed               1
Rascal             1
Rufio              1
Chuck              1
Eriq               1
Aja                1
Beckham            1
Pupcasso           1
Autumn             1
Champ              1
Opie               1
Shawwn             1
Harlso             1
Moofasa            1
Poppy              1
Hanz               1
Todo               1
Lolo               1
Hazel              1
Staniel            1
Dunkin             1
Donny              1
Brandonald         1
Chadrick           1
Crawford           1
Robin              1
Bauer              1
Marlee             1
Fiji               1
Tripp              1
Geno               1
Sweet              1
Brandi             1
Taco               1
Sunshine           1
Molly              1
Gunner             1
Beya               1
Tayzie             1
Mutt               1
Daniel             1
Malikai            1
Ester              1
Saydee             1
Gustav             1
Mac                1
Biden              1
Lambeau            1
Furzey             1
Pinot              1
Lipton             1
Clybe              1
Huck               1
Georgie            1
Fillup             1
Julius             1
Toffee             1
Smiley             1
Puff               1
Jessiga            1
Augie              1
Gordon             1
Ed                 1
Iroh               1
Holly              1
Pilot              1
Venti              1
Petrick            1
Hamrick            1
Ito                1
Ole                1
Buddah             1
Bert               1
Jazzy              1
Charleson          1
Carter             1
Mojo               1
Shelby             1
Amy                1
Ralphson           1
Bayley             1
Tassy              1
Dwight             1
Rudy               1
Bowie              1
Cannon             1
Tess               1
Margo              1
Tanner             1
Aiden              1
Finnegus           1
Sandra             1
Bobby              1
Jameson            1
Devón              1
Zara               1
Rumpole            1
Sweets             1
Anna               1
Loomis             1
Zooey              1
Henry              1
Ike                1
Mark               1
Rey                1
Kuyu               1
Maude              1
Derby              1
Craig              1
Ricky              1
Ferg               1
Kanu               1
Ben                1
Pete               1
Wiggles            1
Rontu              1
Spark              1
Simba              1
Sully              1
Tino               1
Kaia               1
Lassie             1
Swagger            1
Olaf               1
Colin              1
Charl              1
Kathmandu          1
Dido               1
Bradley            1
Zoe                1
Trevith            1
Chase              1
Shikha             1
Jed                1
Ginger             1
Mimosa             1
Willie             1
Durg               1
Divine             1
Tater              1
Jim                1
Mollie             1
officially         1
Jerome             1
Alfy               1
Berb               1
Maks               1
Siba               1
his                1
Kawhi              1
Darla              1
Remus              1
Cleopatricia       1
Cecil              1
Alf                1
Flurpson           1
Amélie             1
Jo                 1
Lugan              1
Stewie             1
Rover              1
Apollo             1
Dudley             1
Burt               1
Vince              1
Ashleigh           1
Barry              1
Miguel             1
Nida               1
Blu                1
Glenn              1
Filup              1
Jonah              1
Adele              1
Stu                1
Tedders            1
Fletcher           1
Gin                1
Doobert            1
Kane               1
Maya               1
Florence           1
Mookie             1
Zeus               1
Strudel            1
Fido               1
Sprinkles          1
Pavlov             1
Blakely            1
Huxley             1
Callie             1
Tessa              1
Asher              1
Tobi               1
incredibly         1
Dutch              1
Rodman             1
Brudge             1
Marty              1
Kial               1
Peanut             1
Tango              1
Lili               1
Yoda               1
Billl              1
Timmy              1
Andy               1
Rambo              1
Goliath            1
Jeffri             1
Millie             1
Scruffers          1
Evy                1
Herb               1
Horace             1
Theo               1
Napolean           1
Buckley            1
Thor               1
Brat               1
Kenzie             1
Bodie              1
Blue               1
Joey               1
Vinscent           1
Katie              1
Eleanor            1
Rilo               1
Bookstore          1
Izzy               1
Comet              1
Kara               1
Taz                1
Sora               1
Rodney             1
Miley              1
Ambrose            1
Rolf               1
Shakespeare        1
Striker            1
Ralphie            1
Jeremy             1
Tebow              1
Joshwa             1
General            1
Obi                1
Dietrich           1
Ralphus            1
Jangle             1
Mingus             1
JD                 1
Berkeley           1
Butters            1
Kirk               1
Bloop              1
Mitch              1
Boots              1
Rorie              1
Reptar             1
Tycho              1
Rose               1
Barney             1
Marq               1
Duchess            1
Dixie              1
Perry              1
Lillie             1
Noah               1
Brandy             1
Longfellow         1
Benny              1
Noosh              1
Ziva               1
Dex                1
Kayla              1
Meatball           1
Chesterson         1
Rueben             1
Crouton            1
Gabby              1
Trip               1
Brady              1
Lenox              1
Howie              1
Reagan             1
Geoff              1
Ralphé             1
Socks              1
Cedrick            1
Jomathan           1
Lacy               1
Nugget             1
Brockly            1
Pubert             1
space              1
Humphrey           1
Lizzie             1
Jackie             1
Roscoe             1
Chevy              1
Flash              1
Fynn               1
Bobbay             1
Corey              1
Ridley             1
Rupert             1
Alexanderson       1
Grizzie            1
Koko               1
Pherb              1
Eugene             1
Stuart             1
Sid                1
Acro               1
Billy              1
Goose              1
Hall               1
Oreo               1
Diogi              1
Lucky              1
Bobble             1
Grey               1
Walker             1
Aldrick            1
Cupid              1
O                  1
Clarq              1
Kellogg            1
Kloey              1
Jimbo              1
Teddy              1
Creg               1
Willy              1
Vinnie             1
Jarvis             1
Anthony            1
Freddery           1
Strider            1
Carll              1
Cilantro           1
Torque             1
Hero               1
Bloo               1
Chloe              1
Christoper         1
Sundance           1
Dewey              1
Karma              1
Terrenth           1
Kona               1
Wishes             1
Pepper             1
by                 1
Tyrus              1
Snickers           1
Pippin             1
Arlo               1
Tove               1
Suki               1
Bobb               1
Dylan              1
Tommy              1
Genevieve          1
Pumpkin            1
Willow             1
Ronnie             1
Birf               1
Amber              1
Odin               1
Beemo              1
Ralf               1
Sonny              1
Shooter            1
Newt               1
Gerbald            1
Combo              1
Einstein           1
Pip                1
Karll              1
Mauve              1
Mary               1
Linus              1
Caryl              1
Bonaparte          1
Arnold             1
Stark              1
Bruiser            1
Mona               1
Ozzie              1
light              1
Michelangelope     1
Naphaniel          1
Coopson            1
Kollin             1
Obie               1
Norman             1
Griswold           1
Murphy             1
Winifred           1
Pawnd              1
Darrel             1
Monty              1
Ralpher            1
Stephanus          1
Lulu               1
Jazz               1
Andru              1
Ron                1
Jebberson          1
Edmund             1
Pancake            1
Jiminus            1
Aubie              1
Jeffrie            1
Brownie            1
Travis             1
Jay                1
River              1
Storkson           1
Crimson            1
Trigger            1
Leonidas           1
Milky              1
Carper             1
Ulysses            1
Dot                1
Banditt            1
Claude             1
Samsom             1
Vixen              1
Hermione           1
Tupawc             1
Sprout             1
Rooney             1
Zeek               1
Ruffles            1
Leonard            1
Grizzwald          1
Shnuggles          1
Patch              1
Blanket            1
BeBe               1
Covach             1
Randall            1
Baron              1
Brian              1
Banjo              1
Laika              1
Aqua               1
Crumpet            1
Binky              1
Kota               1
Gustaf             1
Julio              1
Snicku             1
Harnold            1
Gert               1
Rinna              1
Beebop             1
Philippe           1
Bilbo              1
Harry              1
Arlen              1
Josep              1
Orion              1
Emanuel            1
Blipson            1
Godi               1
Superpup           1
Kallie             1
Dotsy              1
Nico               1
Damon              1
Monkey             1
Skye               1
Sailer             1
Mosby              1
Kulet              1
Deacon             1
Livvie             1
Jockson            1
Ralphy             1
DayZ               1
Tito               1
Godzilla           1
Ivar               1
Grady              1
Sojourner          1
Skittles           1
Emmie              1
Keet               1
Sobe               1
Jersey             1
Jaycob             1
Jordy              1
Mack               1
Liam               1
Boston             1
Lucia              1
Erik               1
Stormy             1
Kevon              1
Willem             1
Dug                1
Ronduh             1
Fabio              1
Duddles            1
Kingsley           1
Farfle             1
Bertson            1
Nigel              1
Harrison           1
Kody               1
Maisey             1
Mairi              1
Bradlay            1
Grizz              1
Tonks              1
Stella             1
Enchilada          1
Ebby               1
Eazy               1
Jessifer           1
Chaz               1
Lance              1
Kramer             1
Lilli              1
Ralph              1
Cuddles            1
Harvey             1
Luther             1
Barclay            1
Yukon              1
Name: name, dtype: int64

Quality issue¶

Define:¶

Original tweets only. Rows with retweets and replay should be removed

code¶

# removing retweets rows
twitter_archive_df_clean = twitter_archive_df_clean[twitter_archive_df_clean.retweeted_status_id.isnull()]
# removing replays rows
twitter_archive_df_clean = twitter_archive_df_clean[twitter_archive_df_clean.in_reply_to_status_id.isnull()]

Test¶

twitter_archive_df_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2097 entries, 0 to 2355
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype         
---  ------                      --------------  -----         
 0   tweet_id                    2097 non-null   int64         
 1   in_reply_to_status_id       0 non-null      float64       
 2   in_reply_to_user_id         0 non-null      float64       
 3   timestamp                   2097 non-null   datetime64[ns]
 4   source                      2097 non-null   object        
 5   text                        2097 non-null   object        
 6   retweeted_status_id         0 non-null      float64       
 7   retweeted_status_user_id    0 non-null      float64       
 8   retweeted_status_timestamp  0 non-null      object        
 9   expanded_urls               2094 non-null   object        
 10  rating_numerator            2097 non-null   int64         
 11  rating_denominator          2097 non-null   int64         
 12  name                        1419 non-null   object        
 13  doggo                       2097 non-null   object        
 14  floofer                     2097 non-null   object        
 15  pupper                      2097 non-null   object        
 16  puppo                       2097 non-null   object        
dtypes: datetime64[ns](1), float64(4), int64(3), object(9)
memory usage: 294.9+ KB

Quality issue¶

Define:¶

Removing (in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp) related to replays and retweets, because only the original tweets to be kept.

code¶

# drop columns that related to retweets and replay
twitter_archive_df_clean = twitter_archive_df_clean.drop(columns=['in_reply_to_status_id','in_reply_to_user_id',
                                                                  'retweeted_status_id','retweeted_status_user_id',
                                                                  'retweeted_status_timestamp'])

Test¶

twitter_archive_df_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2097 entries, 0 to 2355
Data columns (total 12 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   tweet_id            2097 non-null   int64         
 1   timestamp           2097 non-null   datetime64[ns]
 2   source              2097 non-null   object        
 3   text                2097 non-null   object        
 4   expanded_urls       2094 non-null   object        
 5   rating_numerator    2097 non-null   int64         
 6   rating_denominator  2097 non-null   int64         
 7   name                1419 non-null   object        
 8   doggo               2097 non-null   object        
 9   floofer             2097 non-null   object        
 10  pupper              2097 non-null   object        
 11  puppo               2097 non-null   object        
dtypes: datetime64[ns](1), int64(3), object(8)
memory usage: 213.0+ KB

Quality issue¶

Define¶

some dogs appeare in more than one stage e.x. (doggo and floofer). Therefore, firstly getting rid of None with in the columns (doggo,floofer,pupper,puppo)

twitter_archive_df_clean.query('doggo == doggo').count()[8:12]

doggo      2097
floofer    2097
pupper     2097
puppo      2097
dtype: int64

twitter_archive_df_clean.query('floofer == floofer').count()[8:12]

doggo      2097
floofer    2097
pupper     2097
puppo      2097
dtype: int64

twitter_archive_df_clean.query('pupper == pupper').count()[8:12]

doggo      2097
floofer    2097
pupper     2097
puppo      2097
dtype: int64

twitter_archive_df_clean.query('puppo == puppo').count()[8:12]

doggo      2097
floofer    2097
pupper     2097
puppo      2097
dtype: int64

Code¶

# handle none
twitter_archive_df_clean.doggo.replace('None', '', inplace=True)
twitter_archive_df_clean.floofer.replace('None', '', inplace=True)
twitter_archive_df_clean.pupper.replace('None', '', inplace=True)
twitter_archive_df_clean.puppo.replace('None', '', inplace=True)

Test¶

twitter_archive_df_clean.query('doggo == doggo').sample(10)

Tidiness issue¶

define¶

doggo, floofer, pupper and puppo should be represented in one column as dog_stage

code¶

# merge into column
twitter_archive_df_clean['dog_stage'] = twitter_archive_df_clean.doggo + twitter_archive_df_clean.floofer + twitter_archive_df_clean.pupper + twitter_archive_df_clean.puppo

Test¶

twitter_archive_df_clean

Quality issue¶

Define¶

some of the values dog_stage column have two value in it e.x(doggopuppo) will have (,) to separate it.

twitter_archive_df_clean.dog_stage[191]

'doggopuppo'

code¶

# handle multiple stages
twitter_archive_df_clean.loc[twitter_archive_df_clean.dog_stage == 'doggopupper', 'dog_stage'] = 'doggo, pupper'
twitter_archive_df_clean.loc[twitter_archive_df_clean.dog_stage == 'doggopuppo', 'dog_stage'] = 'doggo, puppo'
twitter_archive_df_clean.loc[twitter_archive_df_clean.dog_stage == 'doggofloofer', 'dog_stage'] = 'doggo, floofer'

Test¶

twitter_archive_df_clean.dog_stage[191]

'doggo, puppo'

Quality issue¶

Define¶

There are empty values in dog_stage column. It should be replace with NaN

code¶

# handle missing values
twitter_archive_df_clean.loc[twitter_archive_df_clean.dog_stage == '', 'dog_stage'] = np.nan

Test¶

twitter_archive_df_clean.dog_stage.sample(10)

1101       NaN
1167       NaN
1483    pupper
1884       NaN
639        NaN
1823       NaN
896        NaN
1082       NaN
1002       NaN
300      doggo
Name: dog_stage, dtype: object

Quality issue¶

Define¶

image_predictionsdf columns(p1,p2,p3): showing first letter issue (capital and small letter) and underscore () need to be replaced with space

code¶

col_list = ['p1','p2','p3']
for col in col_list:
    image_predictions_df_clean[col] = image_predictions_df_clean[col].str.replace('_', " ")
    image_predictions_df_clean[col] = image_predictions_df_clean[col].str.capitalize()

Test¶

image_predictions_df_clean.sample(10)

Quality issue¶

Define:¶

image_predictions_df: This is to convert the result of predictions into one column breed_dog. Relaying on (p1_dog,P2_dog,p3_dog) if it's true it will take the name from the name related to this prediction, which can be found in (p1,p2,p3). breed_dog will take the name from the first true prediction.

code¶

def dog_breed(row):
    if row['p1_dog']:
        return row['p1']
    elif row['p2_dog']:
        return row['p2']
    elif row['p3_dog']:
        return row['p3']
    else:
        return np.nan
    
    
image_predictions_df_clean['breed_dog'] = image_predictions_df_clean.apply(dog_breed ,axis=1)

Test¶

image_predictions_df_clean.sample(10)

Quality¶

Define¶

(id, retweet_count and favorite_count) these the only columns that should be kept, other columns will be removed.

tweet_df_clean.columns

Index(['created_at', 'id', 'id_str', 'full_text', 'truncated',
       'display_text_range', 'entities', 'extended_entities', 'source',
       'in_reply_to_status_id', 'in_reply_to_status_id_str',
       'in_reply_to_user_id', 'in_reply_to_user_id_str',
       'in_reply_to_screen_name', 'user', 'geo', 'coordinates', 'place',
       'contributors', 'is_quote_status', 'retweet_count', 'favorite_count',
       'favorited', 'retweeted', 'possibly_sensitive',
       'possibly_sensitive_appealable', 'lang', 'retweeted_status',
       'quoted_status_id', 'quoted_status_id_str', 'quoted_status'],
      dtype='object')

code¶

tweet_df_clean = tweet_df_clean.drop(columns=['created_at', 'id_str', 'full_text', 'truncated',
                                              'display_text_range', 'entities', 'extended_entities', 'source',
                                              'in_reply_to_status_id', 'in_reply_to_status_id_str',
                                              'in_reply_to_user_id', 'in_reply_to_user_id_str',
                                              'in_reply_to_screen_name', 'user', 'geo', 'coordinates', 'place',
                                              'contributors', 'is_quote_status','favorited', 'retweeted',
                                              'possibly_sensitive','possibly_sensitive_appealable', 'lang', 
                                              'retweeted_status','quoted_status_id', 'quoted_status_id_str',
                                              'quoted_status'])

Test¶

tweet_df_clean.columns

Index(['id', 'retweet_count', 'favorite_count'], dtype='object')

tweet_df_clean.head()

Quality issue¶

Define:¶

Rename id column to be tweet_id

code¶

tweet_df_clean.rename(columns={'id':'tweet_id'}, inplace=True)

Test¶

tweet_df_clean.head()

Tidiness issue¶

Define:¶

Joining tables with twitter_archive_df_clean the main dataframe.Main dataframe will be created, which will contain the 3 dataframe

code¶

main_df_clean = pd.merge(twitter_archive_df_clean, image_predictions_df_clean, how= 'inner', on='tweet_id')
main_df_clean = pd.merge(main_df_clean, tweet_df_clean, how='inner', on='tweet_id')

Test¶

main_df_clean.head()

Storing¶

main_df_clean.to_csv('twitter_archive_master.csv')

Qualities and Tidiness Summary¶

Analyzing¶

main_df_clean.describe()

Tweets Sources¶

98% percent of the tweets came from iphone

main_df_clean.source.value_counts().plot(kind='pie',figsize=(10,10), fontsize=10, autopct='%1.1f%%')
plt.title('Tweets Sources', size=15)
plt.ylabel('');

Dog names¶

plt.figure(figsize=(12,10))
sns.barplot(y=main_df_clean.name.value_counts().head(5).values, x=main_df_clean.name.value_counts().head(5).index, alpha=0.7)
plt.title('Top 5 Frequent Dogs name', size=15)
plt.xlabel('Dog name', size=12)
plt.ylabel('Frequency', size=12)
plt.show();

Charlie is the most common dog's name

Favourite count and Retweets¶

plt.figure(figsize=(10,7))
plt.subplot()
plt.title('Retweets', size=15)
sns.distplot(main_df_clean.retweet_count, color = 'green');

/Users/abdulazizalabdullatif/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

plt.figure(figsize=(10,7))
plt.subplot()
plt.title('Favorite', size=15)
sns.distplot(main_df_clean.favorite_count, color = 'blue');

/Users/abdulazizalabdullatif/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

# Correlation between numerical 'retweet_count','favorite_count', 'rating_numerator','rating_denominator'
num_cols_data = (main_df_clean[['retweet_count','favorite_count', 'rating_numerator','rating_denominator']])
matrix = num_cols_data.corr()
f, ax = plt.subplots(figsize=(20, 10))

sns.heatmap(matrix, vmax=.8, square=True, cmap="Blues");

As shown in the above charts and heatmap there is a strong correlation between favourite and retweet.

plt.figure(figsize=(15,10))
main_df_clean.retweet_count.groupby([main_df_clean.timestamp.dt.year, main_df_clean.timestamp.dt.month]).mean().plot(kind='line')
main_df_clean.favorite_count.groupby([main_df_clean.timestamp.dt.year, main_df_clean.timestamp.dt.month]).mean().plot(kind='line')
plt.title('The change of Retweet and Favourite count during time')
plt.xlabel('Time (year,month)')
plt.ylabel('Tweets')
plt.legend(('Retweet count','Favourite count'), fontsize=15);

This is shows that retweets and favourite both increases during time. However, there is a big different in number, which keep increasing during time pass.

Dog Stage¶

main_df_clean.groupby(['dog_stage']).breed_dog.count()

dog_stage
doggo              54
doggo, floofer      1
doggo, pupper       7
doggo, puppo        1
floofer             7
pupper            166
puppo              21
Name: breed_dog, dtype: int64

Due to the high number of pupper stage dogs, they are showing more number on breed dog

Dog stage (Retweet annd Favorite)¶

plt.figure(figsize=(15,10))
sns.boxplot(x=main_df_clean.dog_stage, y=main_df_clean.retweet_count)
plt.title('Retweets for each Dog Stages', size=13)
plt.xlabel('Dog Stage')
plt.ylabel('Retweets')
plt.ylim(0,13000);

plt.figure(figsize=(15,10))
sns.boxplot(x=main_df_clean.dog_stage, y=main_df_clean.favorite_count)
plt.title('Favorite count for each Dog stage', size=13)
plt.ylabel('Favorite count')
plt.xlabel('Dog Stage')
plt.ylim(0,50000);

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	retweeted_status_id	retweeted_status_user_id	rating_numerator	rating_denominator
count	2.356000e+03	7.800000e+01	7.800000e+01	1.810000e+02	1.810000e+02	2356.000000	2356.000000
mean	7.427716e+17	7.455079e+17	2.014171e+16	7.720400e+17	1.241698e+16	13.126486	10.455433
std	6.856705e+16	7.582492e+16	1.252797e+17	6.236928e+16	9.599254e+16	45.876648	6.745237
min	6.660209e+17	6.658147e+17	1.185634e+07	6.661041e+17	7.832140e+05	0.000000	0.000000
25%	6.783989e+17	6.757419e+17	3.086374e+08	7.186315e+17	4.196984e+09	10.000000	10.000000
50%	7.196279e+17	7.038708e+17	4.196984e+09	7.804657e+17	4.196984e+09	11.000000	10.000000
75%	7.993373e+17	8.257804e+17	4.196984e+09	8.203146e+17	4.196984e+09	12.000000	10.000000
max	8.924206e+17	8.862664e+17	8.405479e+17	8.874740e+17	7.874618e+17	1776.000000	170.000000

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True

	tweet_id	img_num	p1_conf	p2_conf	p3_conf
count	2.075000e+03	2075.000000	2075.000000	2.075000e+03	2.075000e+03
mean	7.384514e+17	1.203855	0.594548	1.345886e-01	6.032417e-02
std	6.785203e+16	0.561875	0.271174	1.006657e-01	5.090593e-02
min	6.660209e+17	1.000000	0.044333	1.011300e-08	1.740170e-10
25%	6.764835e+17	1.000000	0.364412	5.388625e-02	1.622240e-02
50%	7.119988e+17	1.000000	0.588230	1.181810e-01	4.944380e-02
75%	7.932034e+17	1.000000	0.843855	1.955655e-01	9.180755e-02
max	8.924206e+17	4.000000	1.000000	4.880140e-01	2.734190e-01

	id	id_str	in_reply_to_status_id	in_reply_to_status_id_str	in_reply_to_user_id	in_reply_to_user_id_str	geo	coordinates	contributors	retweet_count	favorite_count	possibly_sensitive	possibly_sensitive_appealable	quoted_status_id	quoted_status_id_str
count	2.354000e+03	2.354000e+03	7.800000e+01	7.800000e+01	7.800000e+01	7.800000e+01	0.0	0.0	0.0	2354.000000	2354.000000	2211.0	2211.0	2.900000e+01	2.900000e+01
mean	7.426978e+17	7.426978e+17	7.455079e+17	7.455079e+17	2.014171e+16	2.014171e+16	NaN	NaN	NaN	3164.797366	8080.968564	0.0	0.0	8.162686e+17	8.162686e+17
std	6.852812e+16	6.852812e+16	7.582492e+16	7.582492e+16	1.252797e+17	1.252797e+17	NaN	NaN	NaN	5284.770364	11814.771334	0.0	0.0	6.164161e+16	6.164161e+16
min	6.660209e+17	6.660209e+17	6.658147e+17	6.658147e+17	1.185634e+07	1.185634e+07	NaN	NaN	NaN	0.000000	0.000000	0.0	0.0	6.721083e+17	6.721083e+17
25%	6.783975e+17	6.783975e+17	6.757419e+17	6.757419e+17	3.086374e+08	3.086374e+08	NaN	NaN	NaN	624.500000	1415.000000	0.0	0.0	7.888183e+17	7.888183e+17
50%	7.194596e+17	7.194596e+17	7.038708e+17	7.038708e+17	4.196984e+09	4.196984e+09	NaN	NaN	NaN	1473.500000	3603.500000	0.0	0.0	8.340867e+17	8.340867e+17
75%	7.993058e+17	7.993058e+17	8.257804e+17	8.257804e+17	4.196984e+09	4.196984e+09	NaN	NaN	NaN	3652.000000	10122.250000	0.0	0.0	8.664587e+17	8.664587e+17
max	8.924206e+17	8.924206e+17	8.862664e+17	8.862664e+17	8.405479e+17	8.405479e+17	NaN	NaN	NaN	79515.000000	132810.000000	0.0	0.0	8.860534e+17	8.860534e+17

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
0	892420643555336193	NaN	NaN	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892420643555336193/photo/1	13	10	Phineas	None	None	None	None
1	892177421306343426	NaN	NaN	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892177421306343426/photo/1	13	10	Tilly	None	None	None	None
2	891815181378084864	NaN	NaN	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891815181378084864/photo/1	12	10	Archie	None	None	None	None
3	891689557279858688	NaN	NaN	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891689557279858688/photo/1	13	10	Darla	None	None	None	None
4	891327558926688256	NaN	NaN	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1	12	10	Franklin	None	None	None	None

	created_at	id	id_str	full_text	truncated	display_text_range	entities	extended_entities	source	in_reply_to_status_id	in_reply_to_status_id_str	in_reply_to_user_id	in_reply_to_user_id_str	in_reply_to_screen_name	user	geo	coordinates	place	contributors	is_quote_status	retweet_count	favorite_count	favorited	retweeted	lang	retweeted_status	quoted_status_id	quoted_status_id_str	quoted_status
0	2017-08-01 16:23:56+00:00	892420643555336193	892420643555336192	This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU	False	[0, 85]	{'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': [], 'media': [{'id': 892420639486877696, 'id_str': '892420639486877696', 'indices': [86, 109], 'media_url': 'http://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg', 'url': 'https://t.co/MgUWQ76dJU', 'display_url': 'pic.twitter.com/MgUWQ76dJU', 'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1', 'type': 'photo', 'sizes': {'large': {'w': 540, 'h': 528, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'small': {'w': 540, 'h': 528, 'resize': 'fit'}, 'medium': {'w': 540, 'h': 528, 'resize': 'fit'}}}]}	{'media': [{'id': 892420639486877696, 'id_str': '892420639486877696', 'indices': [86, 109], 'media_url': 'http://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg', 'url': 'https://t.co/MgUWQ76dJU', 'display_url': 'pic.twitter.com/MgUWQ76dJU', 'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1', 'type': 'photo', 'sizes': {'large': {'w': 540, 'h': 528, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'small': {'w': 540, 'h': 528, 'resize': 'fit'}, 'medium': {'w': 540, 'h': 528, 'resize': 'fit'}}}]}	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	NaN	NaN	NaN	NaN	None	{'id': 4196983835, 'id_str': '4196983835', 'name': 'WeRateDogs™ (author)', 'screen_name': 'dog_rates', 'location': 'DM YOUR DOGS, WE WILL RATE', 'description': '#1 Source for Professional Dog Ratings \| STORE: @ShopWeRateDogs \| IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame \| Business: dogratingtwitter@gmail.com', 'url': 'https://t.co/N7sNNHAEXS', 'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS', 'expanded_url': 'http://weratedogs.com', 'display_url': 'weratedogs.com', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 3200889, 'friends_count': 104, 'listed_count': 2784, 'created_at': 'Sun Nov 15 21:41:29 +0000 2015', 'favourites_count': 114031, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': True, 'statuses_count': 5288, 'lang': 'en', 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': '000000', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1501129017', 'profile_link_color': 'F5ABB5', 'profile_sidebar_border_color': '000000', 'profile_sidebar_fill_color': '000000', 'profile_text_color': '000000', 'profile_use_background_image': False, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': True, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none'}	NaN	NaN	None	NaN	False	8853	39467	False	False	en	NaN	NaN	NaN	NaN
1	2017-08-01 00:17:27+00:00	892177421306343426	892177421306343424	This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV	False	[0, 138]	{'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': [], 'media': [{'id': 892177413194625024, 'id_str': '892177413194625024', 'indices': [139, 162], 'media_url': 'http://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg', 'url': 'https://t.co/0Xxu71qeIV', 'display_url': 'pic.twitter.com/0Xxu71qeIV', 'expanded_url': 'https://twitter.com/dog_rates/status/892177421306343426/photo/1', 'type': 'photo', 'sizes': {'large': {'w': 1407, 'h': 1600, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'small': {'w': 598, 'h': 680, 'resize': 'fit'}, 'medium': {'w': 1055, 'h': 1200, 'resize': 'fit'}}}]}	{'media': [{'id': 892177413194625024, 'id_str': '892177413194625024', 'indices': [139, 162], 'media_url': 'http://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg', 'url': 'https://t.co/0Xxu71qeIV', 'display_url': 'pic.twitter.com/0Xxu71qeIV', 'expanded_url': 'https://twitter.com/dog_rates/status/892177421306343426/photo/1', 'type': 'photo', 'sizes': {'large': {'w': 1407, 'h': 1600, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'small': {'w': 598, 'h': 680, 'resize': 'fit'}, 'medium': {'w': 1055, 'h': 1200, 'resize': 'fit'}}}]}	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	NaN	NaN	NaN	NaN	None	{'id': 4196983835, 'id_str': '4196983835', 'name': 'WeRateDogs™ (author)', 'screen_name': 'dog_rates', 'location': 'DM YOUR DOGS, WE WILL RATE', 'description': '#1 Source for Professional Dog Ratings \| STORE: @ShopWeRateDogs \| IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame \| Business: dogratingtwitter@gmail.com', 'url': 'https://t.co/N7sNNHAEXS', 'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS', 'expanded_url': 'http://weratedogs.com', 'display_url': 'weratedogs.com', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 3200889, 'friends_count': 104, 'listed_count': 2784, 'created_at': 'Sun Nov 15 21:41:29 +0000 2015', 'favourites_count': 114031, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': True, 'statuses_count': 5288, 'lang': 'en', 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': '000000', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1501129017', 'profile_link_color': 'F5ABB5', 'profile_sidebar_border_color': '000000', 'profile_sidebar_fill_color': '000000', 'profile_text_color': '000000', 'profile_use_background_image': False, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': True, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none'}	NaN	NaN	None	NaN	False	6514	33819	False	False	en	NaN	NaN	NaN	NaN
2	2017-07-31 00:18:03+00:00	891815181378084864	891815181378084864	This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB	False	[0, 121]	{'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': [], 'media': [{'id': 891815175371796480, 'id_str': '891815175371796480', 'indices': [122, 145], 'media_url': 'http://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg', 'url': 'https://t.co/wUnZnhtVJB', 'display_url': 'pic.twitter.com/wUnZnhtVJB', 'expanded_url': 'https://twitter.com/dog_rates/status/891815181378084864/photo/1', 'type': 'photo', 'sizes': {'medium': {'w': 901, 'h': 1200, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 1201, 'h': 1600, 'resize': 'fit'}, 'small': {'w': 510, 'h': 680, 'resize': 'fit'}}}]}	{'media': [{'id': 891815175371796480, 'id_str': '891815175371796480', 'indices': [122, 145], 'media_url': 'http://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg', 'url': 'https://t.co/wUnZnhtVJB', 'display_url': 'pic.twitter.com/wUnZnhtVJB', 'expanded_url': 'https://twitter.com/dog_rates/status/891815181378084864/photo/1', 'type': 'photo', 'sizes': {'medium': {'w': 901, 'h': 1200, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 1201, 'h': 1600, 'resize': 'fit'}, 'small': {'w': 510, 'h': 680, 'resize': 'fit'}}}]}	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	NaN	NaN	NaN	NaN	None	{'id': 4196983835, 'id_str': '4196983835', 'name': 'WeRateDogs™ (author)', 'screen_name': 'dog_rates', 'location': 'DM YOUR DOGS, WE WILL RATE', 'description': '#1 Source for Professional Dog Ratings \| STORE: @ShopWeRateDogs \| IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame \| Business: dogratingtwitter@gmail.com', 'url': 'https://t.co/N7sNNHAEXS', 'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS', 'expanded_url': 'http://weratedogs.com', 'display_url': 'weratedogs.com', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 3200889, 'friends_count': 104, 'listed_count': 2784, 'created_at': 'Sun Nov 15 21:41:29 +0000 2015', 'favourites_count': 114031, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': True, 'statuses_count': 5288, 'lang': 'en', 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': '000000', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1501129017', 'profile_link_color': 'F5ABB5', 'profile_sidebar_border_color': '000000', 'profile_sidebar_fill_color': '000000', 'profile_text_color': '000000', 'profile_use_background_image': False, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': True, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none'}	NaN	NaN	None	NaN	False	4328	25461	False	False	en	NaN	NaN	NaN	NaN
3	2017-07-30 15:58:51+00:00	891689557279858688	891689557279858688	This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ	False	[0, 79]	{'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': [], 'media': [{'id': 891689552724799489, 'id_str': '891689552724799489', 'indices': [80, 103], 'media_url': 'http://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg', 'url': 'https://t.co/tD36da7qLQ', 'display_url': 'pic.twitter.com/tD36da7qLQ', 'expanded_url': 'https://twitter.com/dog_rates/status/891689557279858688/photo/1', 'type': 'photo', 'sizes': {'medium': {'w': 901, 'h': 1200, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 1201, 'h': 1600, 'resize': 'fit'}, 'small': {'w': 510, 'h': 680, 'resize': 'fit'}}}]}	{'media': [{'id': 891689552724799489, 'id_str': '891689552724799489', 'indices': [80, 103], 'media_url': 'http://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg', 'url': 'https://t.co/tD36da7qLQ', 'display_url': 'pic.twitter.com/tD36da7qLQ', 'expanded_url': 'https://twitter.com/dog_rates/status/891689557279858688/photo/1', 'type': 'photo', 'sizes': {'medium': {'w': 901, 'h': 1200, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 1201, 'h': 1600, 'resize': 'fit'}, 'small': {'w': 510, 'h': 680, 'resize': 'fit'}}}]}	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	NaN	NaN	NaN	NaN	None	{'id': 4196983835, 'id_str': '4196983835', 'name': 'WeRateDogs™ (author)', 'screen_name': 'dog_rates', 'location': 'DM YOUR DOGS, WE WILL RATE', 'description': '#1 Source for Professional Dog Ratings \| STORE: @ShopWeRateDogs \| IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame \| Business: dogratingtwitter@gmail.com', 'url': 'https://t.co/N7sNNHAEXS', 'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS', 'expanded_url': 'http://weratedogs.com', 'display_url': 'weratedogs.com', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 3200889, 'friends_count': 104, 'listed_count': 2784, 'created_at': 'Sun Nov 15 21:41:29 +0000 2015', 'favourites_count': 114031, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': True, 'statuses_count': 5288, 'lang': 'en', 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': '000000', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1501129017', 'profile_link_color': 'F5ABB5', 'profile_sidebar_border_color': '000000', 'profile_sidebar_fill_color': '000000', 'profile_text_color': '000000', 'profile_use_background_image': False, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': True, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none'}	NaN	NaN	None	NaN	False	8964	42908	False	False	en	NaN	NaN	NaN	NaN
4	2017-07-29 16:00:24+00:00	891327558926688256	891327558926688256	This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f	False	[0, 138]	{'hashtags': [{'text': 'BarkWeek', 'indices': [129, 138]}], 'symbols': [], 'user_mentions': [], 'urls': [], 'media': [{'id': 891327551943041024, 'id_str': '891327551943041024', 'indices': [139, 162], 'media_url': 'http://pbs.twimg.com/media/DF6hr6AVYAAZ8G8.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DF6hr6AVYAAZ8G8.jpg', 'url': 'https://t.co/AtUZn91f7f', 'display_url': 'pic.twitter.com/AtUZn91f7f', 'expanded_url': 'https://twitter.com/dog_rates/status/891327558926688256/photo/1', 'type': 'photo', 'sizes': {'small': {'w': 680, 'h': 510, 'resize': 'fit'}, 'large': {'w': 720, 'h': 540, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'medium': {'w': 720, 'h': 540, 'resize': 'fit'}}}]}	{'media': [{'id': 891327551943041024, 'id_str': '891327551943041024', 'indices': [139, 162], 'media_url': 'http://pbs.twimg.com/media/DF6hr6AVYAAZ8G8.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DF6hr6AVYAAZ8G8.jpg', 'url': 'https://t.co/AtUZn91f7f', 'display_url': 'pic.twitter.com/AtUZn91f7f', 'expanded_url': 'https://twitter.com/dog_rates/status/891327558926688256/photo/1', 'type': 'photo', 'sizes': {'small': {'w': 680, 'h': 510, 'resize': 'fit'}, 'large': {'w': 720, 'h': 540, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'medium': {'w': 720, 'h': 540, 'resize': 'fit'}}}, {'id': 891327551947157504, 'id_str': '891327551947157504', 'indices': [139, 162], 'media_url': 'http://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg', 'media_url_https': 'https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg', 'url': 'https://t.co/AtUZn91f7f', 'display_url': 'pic.twitter.com/AtUZn91f7f', 'expanded_url': 'https://twitter.com/dog_rates/status/891327558926688256/photo/1', 'type': 'photo', 'sizes': {'small': {'w': 680, 'h': 510, 'resize': 'fit'}, 'large': {'w': 720, 'h': 540, 'resize': 'fit'}, 'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'medium': {'w': 720, 'h': 540, 'resize': 'fit'}}}]}	<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>	NaN	NaN	NaN	NaN	None	{'id': 4196983835, 'id_str': '4196983835', 'name': 'WeRateDogs™ (author)', 'screen_name': 'dog_rates', 'location': 'DM YOUR DOGS, WE WILL RATE', 'description': '#1 Source for Professional Dog Ratings \| STORE: @ShopWeRateDogs \| IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame \| Business: dogratingtwitter@gmail.com', 'url': 'https://t.co/N7sNNHAEXS', 'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS', 'expanded_url': 'http://weratedogs.com', 'display_url': 'weratedogs.com', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 3200889, 'friends_count': 104, 'listed_count': 2784, 'created_at': 'Sun Nov 15 21:41:29 +0000 2015', 'favourites_count': 114031, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': True, 'statuses_count': 5288, 'lang': 'en', 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': '000000', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1501129017', 'profile_link_color': 'F5ABB5', 'profile_sidebar_border_color': '000000', 'profile_sidebar_fill_color': '000000', 'profile_text_color': '000000', 'profile_use_background_image': False, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': True, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none'}	NaN	NaN	None	NaN	False	9774	41048	False	False	en	NaN	NaN	NaN	NaN

	text	rating_numerator
45	This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948	5
695	This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS	75
763	This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq	27
1712	Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD	26

	text	rating_numerator
45	This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948	13.50
695	This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS	9.75
763	This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq	11.27
1712	Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD	11.26

	tweet_id	timestamp	source	text	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper
632	793962221541933056	2016-11-02 23:45:19	Twitter for iPhone	This is Maximus. His face is stuck like that. Tragic really. Great tongue tho. 12/10 would pet firmly https://t.co/xIfrsMNLBR	https://twitter.com/dog_rates/status/793962221541933056/photo/1,https://twitter.com/dog_rates/status/793962221541933056/photo/1	12	10	Maximus
1370	702321140488925184	2016-02-24 02:36:23	Twitter for iPhone	Please enjoy this picture as much as I did. 12/10 https://t.co/7u8mM99Tj5	https://twitter.com/dog_rates/status/702321140488925184/photo/1,https://twitter.com/dog_rates/status/702321140488925184/photo/1,https://twitter.com/dog_rates/status/702321140488925184/photo/1,https://twitter.com/dog_rates/status/702321140488925184/photo/1	12	10	NaN
1524	690597161306841088	2016-01-22 18:09:28	Twitter for iPhone	This is Lolo. She's America af. Behind in science & math but can say whatever she wants on Twitter. 11/10 ...Merica https://t.co/Nwi3SYe8KA	https://twitter.com/dog_rates/status/690597161306841088/photo/1	11	10	Lolo
2301	667044094246576128	2015-11-18 18:17:59	Twitter for iPhone	12/10 gimme now https://t.co/QZAnwgnOMB	https://twitter.com/dog_rates/status/667044094246576128/photo/1	12	10	NaN
1897	674737130913071104	2015-12-09 23:47:22	Twitter for iPhone	Meet Rufio. He is unaware of the pink legless pupper wrapped around him. Might want to get that checked 10/10 & 4/10 https://t.co/KNfLnYPmYh	https://twitter.com/dog_rates/status/674737130913071104/photo/1	10	10	Rufio			pupper
1022	746542875601690625	2016-06-25 03:17:46	Vine - Make a Scene	Here's a golden floofer helping with the groceries. Bed got in way. Still 11/10 helpful af (vid by @categoen) https://t.co/6ZRoZUWFmd	https://vine.co/v/5uZYwqmuDeT	11	10	NaN		floofer
1571	687704180304273409	2016-01-14 18:33:48	Twitter for iPhone	Say hello to Blakely. He thinks that's a hat. Silly pupper. That's a nanner. 9/10 https://t.co/UwOV1jqKRt	https://twitter.com/dog_rates/status/687704180304273409/photo/1	9	10	Blakely			pupper
83	876537666061221889	2017-06-18 20:30:39	Twitter for iPhone	I can say with the pupmost confidence that the doggos who assisted with this search are heroic as h*ck. 14/10 for all https://t.co/8yoc1CNTsu	https://twitter.com/mpstowerham/status/876162994446753793	14	10	NaN
945	752660715232722944	2016-07-12 00:27:52	Twitter for iPhone	Hooman used Pokeball\nwiggle\nwiggle\nDoggo broke free \n10/10 https://t.co/bWSgqnwSHr	https://twitter.com/dog_rates/status/752660715232722944/photo/1,https://twitter.com/dog_rates/status/752660715232722944/photo/1	10	10	NaN	doggo
1137	728046963732717569	2016-05-05 02:21:37	Twitter for iPhone	This is Raymond. He controls fountains with his tongue. 11/10 pretty damn magical https://t.co/9aMxSbOaAZ	https://twitter.com/dog_rates/status/728046963732717569/photo/1	11	10	Raymond

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
531	676821958043033607	https://pbs.twimg.com/media/CWSN-vaXAAA8Ehr.jpg	2	Great pyrenees	0.869804	True	Kuvasz	0.079814	True	Standard poodle	0.013263	True
1774	828046555563323392	https://pbs.twimg.com/media/C33P8PrUcAMiQQs.jpg	3	Patio	0.272972	False	Window screen	0.131295	False	Boathouse	0.046393	False
1651	809448704142938112	https://pbs.twimg.com/media/Czu9RiwVEAA_Okk.jpg	1	Greater swiss mountain dog	0.375415	True	Cardigan	0.134317	True	English springer	0.073697	True
1676	813172488309972993	https://pbs.twimg.com/media/C0j4EESUsAABtMq.jpg	1	Doormat	0.954844	False	Golden retriever	0.026193	True	Cocker spaniel	0.004386	True
334	672169685991993344	https://pbs.twimg.com/media/CVQGv-vUwAEUjCj.jpg	1	Cocker spaniel	0.991011	True	Sussex spaniel	0.004032	True	Miniature poodle	0.001276	True
836	694206574471057408	https://pbs.twimg.com/media/CaJRMPQWIAA1zL9.jpg	1	Shih-tzu	0.352547	True	Toy poodle	0.155720	True	Maltese dog	0.116657	True
1660	811627233043480576	https://pbs.twimg.com/media/C0N6opSXAAAkCtN.jpg	1	Beagle	0.396280	True	Pembroke	0.049562	True	Wire-haired fox terrier	0.046349	True
782	690005060500217858	https://pbs.twimg.com/media/CZNj8N-WQAMXASZ.jpg	1	Samoyed	0.270287	True	Great pyrenees	0.114027	True	Teddy	0.072475	False
1443	775729183532220416	https://pbs.twimg.com/media/CsPxk85XEAAeMQj.jpg	1	Web site	0.989407	False	Hand-held computer	0.002139	False	Menu	0.002115	False
92	667546741521195010	https://pbs.twimg.com/media/CUOaOWXWcAA0_Jy.jpg	1	Toy poodle	0.787424	True	Miniature poodle	0.202225	True	Teddy	0.004047	False

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog	breed_dog
2051	887473957103951883	https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg	2	Pembroke	0.809197	True	Rhodesian ridgeback	0.054950	True	Beagle	0.038915	True	Pembroke
533	676897532954456065	https://pbs.twimg.com/media/CWTSt0UW4AALMNB.jpg	1	Hamster	0.628255	False	Guinea pig	0.318646	False	Macaque	0.013058	False	NaN
267	670807719151067136	https://pbs.twimg.com/media/CU8v-rdXIAId12Z.jpg	1	Old english sheepdog	0.958035	True	Sealyham terrier	0.013892	True	Border collie	0.004601	True	Old english sheepdog
1880	846874817362120707	https://pbs.twimg.com/media/C8C0JYHW0AAy-7u.jpg	2	Shetland sheepdog	0.450539	True	Papillon	0.187928	True	Collie	0.140068	True	Shetland sheepdog
855	696713835009417216	https://pbs.twimg.com/media/Cas5h-wWcAA3nAc.jpg	1	Car mirror	0.379797	False	Chesapeake bay retriever	0.321589	True	Vizsla	0.116931	True	Chesapeake bay retriever
2006	877611172832227328	https://pbs.twimg.com/media/DCszHgmW0AAmIpT.jpg	1	Irish setter	0.364729	True	Golden retriever	0.202907	True	Irish terrier	0.107473	True	Irish setter
1740	822489057087389700	https://pbs.twimg.com/media/C2oRbOuWEAAbVSl.jpg	1	Samoyed	0.416769	True	Malamute	0.252706	True	Kuvasz	0.157028	True	Samoyed
704	684926975086034944	https://pbs.twimg.com/media/CYFZXdiU0AAc_kw.jpg	1	Labrador retriever	0.769412	True	Golden retriever	0.144893	True	Lion	0.021440	False	Labrador retriever
1814	833722901757046785	https://pbs.twimg.com/media/C5H6jmgW8AAevqq.jpg	1	West highland white terrier	0.918144	True	Maltese dog	0.025721	True	Lakeland terrier	0.020211	True	West highland white terrier
157	668852170888998912	https://pbs.twimg.com/media/CUg9gBvWoAAmx-2.jpg	1	Golden retriever	0.903529	True	Tibetan mastiff	0.041497	True	Kuvasz	0.022500	True	Golden retriever

	tweet_id	timestamp	source	text	expanded_urls	rating_numerator	rating_denominator	name	dog_stage	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog	breed_dog	retweet_count	favorite_count
0	892420643555336193	2017-08-01 16:23:56	Twitter for iPhone	This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU	https://twitter.com/dog_rates/status/892420643555336193/photo/1	13	10	Phineas	NaN	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	Orange	0.097049	False	Bagel	0.085851	False	Banana	0.076110	False	NaN	8853	39467
1	892177421306343426	2017-08-01 00:17:27	Twitter for iPhone	This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV	https://twitter.com/dog_rates/status/892177421306343426/photo/1	13	10	Tilly	NaN	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	Papillon	0.068957	True	Chihuahua	6514	33819
2	891815181378084864	2017-07-31 00:18:03	Twitter for iPhone	This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB	https://twitter.com/dog_rates/status/891815181378084864/photo/1	12	10	Archie	NaN	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	Malamute	0.078253	True	Kelpie	0.031379	True	Chihuahua	4328	25461
3	891689557279858688	2017-07-30 15:58:51	Twitter for iPhone	This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ	https://twitter.com/dog_rates/status/891689557279858688/photo/1	13	10	Darla	NaN	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	Paper towel	0.170278	False	Labrador retriever	0.168086	True	Spatula	0.040836	False	Labrador retriever	8964	42908
4	891327558926688256	2017-07-29 16:00:24	Twitter for iPhone	This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f	https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1	12	10	Franklin	NaN	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	Basset	0.555712	True	English springer	0.225770	True	German short-haired pointer	0.175219	True	Basset	9774	41048

	tweet_id	rating_numerator	rating_denominator	img_num	p1_conf	p2_conf	p3_conf	retweet_count	favorite_count
count	1.971000e+03	1971.000000	1971.000000	1971.000000	1971.000000	1.971000e+03	1.971000e+03	1971.000000	1971.000000
mean	7.360418e+17	12.223237	10.477423	1.201928	0.594558	1.345850e-01	6.016556e-02	2784.449518	8949.106545
std	6.752810e+16	41.634034	6.853275	0.559020	0.272126	1.010527e-01	5.094156e-02	4697.662893	12267.799790
min	6.660209e+17	0.000000	2.000000	1.000000	0.044333	1.011300e-08	1.740170e-10	16.000000	81.000000
25%	6.758656e+17	10.000000	10.000000	1.000000	0.363091	5.339800e-02	1.608055e-02	628.500000	1997.000000
50%	7.088343e+17	11.000000	10.000000	1.000000	0.587764	1.173970e-01	4.944380e-02	1367.000000	4147.000000
75%	7.880951e+17	12.000000	10.000000	1.000000	0.847827	1.955655e-01	9.153815e-02	3239.000000	11402.500000
max	8.924206e+17	1776.000000	170.000000	4.000000	1.000000	4.880140e-01	2.734190e-01	79515.000000	132810.000000

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	1.566650e-01	True	Shetland_sheepdog	6.142850e-02	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	7.419170e-02	True	Rhodesian_ridgeback	7.201000e-02	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	1.385840e-01	True	bloodhound	1.161970e-01	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	3.606870e-01	True	miniature_pinscher	2.227520e-01	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	2.436820e-01	True	Doberman	1.546290e-01	True
5	666050758794694657	https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg	1	Bernese_mountain_dog	0.651137	True	English_springer	2.637880e-01	True	Greater_Swiss_Mountain_dog	1.619920e-02	True
6	666051853826850816	https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg	1	box_turtle	0.933012	False	mud_turtle	4.588540e-02	False	terrapin	1.788530e-02	False
7	666055525042405380	https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg	1	chow	0.692517	True	Tibetan_mastiff	5.827940e-02	True	fur_coat	5.444860e-02	False
8	666057090499244032	https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg	1	shopping_cart	0.962465	False	shopping_basket	1.459380e-02	False	golden_retriever	7.958960e-03	True
9	666058600524156928	https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg	1	miniature_poodle	0.201493	True	komondor	1.923050e-01	True	soft-coated_wheaten_terrier	8.208610e-02	True
10	666063827256086533	https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg	1	golden_retriever	0.775930	True	Tibetan_mastiff	9.371780e-02	True	Labrador_retriever	7.242660e-02	True
11	666071193221509120	https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg	1	Gordon_setter	0.503672	True	Yorkshire_terrier	1.742010e-01	True	Pekinese	1.094540e-01	True
12	666073100786774016	https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg	1	Walker_hound	0.260857	True	English_foxhound	1.753820e-01	True	Ibizan_hound	9.747050e-02	True
13	666082916733198337	https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg	1	pug	0.489814	True	bull_mastiff	4.047220e-01	True	French_bulldog	4.895950e-02	True
14	666094000022159362	https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg	1	bloodhound	0.195217	True	German_shepherd	7.825980e-02	True	malinois	7.562780e-02	True
15	666099513787052032	https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg	1	Lhasa	0.582330	True	Shih-Tzu	1.661920e-01	True	Dandie_Dinmont	8.968830e-02	True
16	666102155909144576	https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg	1	English_setter	0.298617	True	Newfoundland	1.498420e-01	True	borzoi	1.336490e-01	True
17	666104133288665088	https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg	1	hen	0.965932	False	cock	3.391940e-02	False	partridge	5.206580e-05	False
18	666268910803644416	https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg	1	desktop_computer	0.086502	False	desk	8.554740e-02	False	bookcase	7.947970e-02	False
19	666273097616637952	https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg	1	Italian_greyhound	0.176053	True	toy_terrier	1.118840e-01	True	basenji	1.111520e-01	True
20	666287406224695296	https://pbs.twimg.com/media/CT8g3BpUEAAuFjg.jpg	1	Maltese_dog	0.857531	True	toy_poodle	6.306380e-02	True	miniature_poodle	2.558060e-02	True
21	666293911632134144	https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg	1	three-toed_sloth	0.914671	False	otter	1.525000e-02	False	great_grey_owl	1.320720e-02	False
22	666337882303524864	https://pbs.twimg.com/media/CT9OwFIWEAMuRje.jpg	1	ox	0.416669	False	Newfoundland	2.784070e-01	True	groenendael	1.026430e-01	True
23	666345417576210432	https://pbs.twimg.com/media/CT9Vn7PWoAA_ZCM.jpg	1	golden_retriever	0.858744	True	Chesapeake_Bay_retriever	5.478680e-02	True	Labrador_retriever	1.424090e-02	True
24	666353288456101888	https://pbs.twimg.com/media/CT9cx0tUEAAhNN_.jpg	1	malamute	0.336874	True	Siberian_husky	1.476550e-01	True	Eskimo_dog	9.341240e-02	True
25	666362758909284353	https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg	1	guinea_pig	0.996496	False	skunk	2.402450e-03	False	hamster	4.608630e-04	False
26	666373753744588802	https://pbs.twimg.com/media/CT9vZEYWUAAlZ05.jpg	1	soft-coated_wheaten_terrier	0.326467	True	Afghan_hound	2.595510e-01	True	briard	2.068030e-01	True
27	666396247373291520	https://pbs.twimg.com/media/CT-D2ZHWIAA3gK1.jpg	1	Chihuahua	0.978108	True	toy_terrier	9.396970e-03	True	papillon	4.576810e-03	True
28	666407126856765440	https://pbs.twimg.com/media/CT-NvwmW4AAugGZ.jpg	1	black-and-tan_coonhound	0.529139	True	bloodhound	2.442200e-01	True	flat-coated_retriever	1.738100e-01	True
29	666411507551481857	https://pbs.twimg.com/media/CT-RugiWIAELEaq.jpg	1	coho	0.404640	False	barracouta	2.714850e-01	False	gar	1.899450e-01	False
30	666418789513326592	https://pbs.twimg.com/media/CT-YWb7U8AA7QnN.jpg	1	toy_terrier	0.149680	True	papillon	1.482580e-01	True	Chihuahua	1.428600e-01	True
31	666421158376562688	https://pbs.twimg.com/media/CT-aggCXAAIMfT3.jpg	1	Blenheim_spaniel	0.906777	True	cocker_spaniel	9.034640e-02	True	Shih-Tzu	1.116870e-03	True
32	666428276349472768	https://pbs.twimg.com/media/CT-g-0DUwAEQdSn.jpg	1	Pembroke	0.371361	True	chow	2.493940e-01	True	Pomeranian	2.418780e-01	True
33	666430724426358785	https://pbs.twimg.com/media/CT-jNYqW4AAPi2M.jpg	1	llama	0.505184	False	Irish_terrier	1.041090e-01	True	dingo	6.207120e-02	False
34	666435652385423360	https://pbs.twimg.com/media/CT-nsTQWEAEkyDn.jpg	1	Chesapeake_Bay_retriever	0.184130	True	chain_saw	5.677530e-02	False	power_drill	3.676340e-02	False
35	666437273139982337	https://pbs.twimg.com/media/CT-pKmRWIAAxUWj.jpg	1	Chihuahua	0.671853	True	beagle	1.246800e-01	True	Saluki	4.409420e-02	True
36	666447344410484738	https://pbs.twimg.com/media/CT-yU5QWwAEjLX5.jpg	1	curly-coated_retriever	0.322084	True	giant_schnauzer	2.879550e-01	True	Labrador_retriever	1.663310e-01	True
37	666454714377183233	https://pbs.twimg.com/media/CT-5Bs-WUAA2JeC.jpg	1	dalmatian	0.278954	True	Labrador_retriever	2.376120e-01	True	Great_Pyrenees	1.711060e-01	True
38	666644823164719104	https://pbs.twimg.com/media/CUBl6IwVAAA9_zT.jpg	1	Ibizan_hound	0.044333	True	Pembroke	4.320930e-02	True	West_Highland_white_terrier	3.890560e-02	True
39	666649482315059201	https://pbs.twimg.com/media/CUBqKnLWwAA5OQB.jpg	1	Border_collie	0.447803	True	English_springer	1.704970e-01	True	collie	1.392060e-01	True

	tweet_id	timestamp	source	text	expanded_urls	rating_numerator	rating_denominator	name	doggo	pupper	puppo	dog_stage
0	892420643555336193	2017-08-01 16:23:56	Twitter for iPhone	This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU	https://twitter.com/dog_rates/status/892420643555336193/photo/1	13	10	Phineas
1	892177421306343426	2017-08-01 00:17:27	Twitter for iPhone	This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV	https://twitter.com/dog_rates/status/892177421306343426/photo/1	13	10	Tilly
2	891815181378084864	2017-07-31 00:18:03	Twitter for iPhone	This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB	https://twitter.com/dog_rates/status/891815181378084864/photo/1	12	10	Archie
3	891689557279858688	2017-07-30 15:58:51	Twitter for iPhone	This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ	https://twitter.com/dog_rates/status/891689557279858688/photo/1	13	10	Darla
4	891327558926688256	2017-07-29 16:00:24	Twitter for iPhone	This is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f	https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1	12	10	Franklin
5	891087950875897856	2017-07-29 00:08:17	Twitter for iPhone	Here we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmh	https://twitter.com/dog_rates/status/891087950875897856/photo/1	13	10	NaN
6	890971913173991426	2017-07-28 16:27:12	Twitter for iPhone	Meet Jax. He enjoys ice cream so much he gets nervous around it. 13/10 help Jax enjoy more things by clicking below\n\nhttps://t.co/Zr4hWfAs1H https://t.co/tVJBRMnhxl	https://gofundme.com/ydvmve-surgery-for-jax,https://twitter.com/dog_rates/status/890971913173991426/photo/1	13	10	Jax
7	890729181411237888	2017-07-28 00:22:40	Twitter for iPhone	When you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxq	https://twitter.com/dog_rates/status/890729181411237888/photo/1,https://twitter.com/dog_rates/status/890729181411237888/photo/1	13	10	NaN
8	890609185150312448	2017-07-27 16:25:51	Twitter for iPhone	This is Zoey. She doesn't want to be one of the scary sharks. Just wants to be a snuggly pettable boatpet. 13/10 #BarkWeek https://t.co/9TwLuAGH0b	https://twitter.com/dog_rates/status/890609185150312448/photo/1	13	10	Zoey
9	890240255349198849	2017-07-26 15:59:51	Twitter for iPhone	This is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2A	https://twitter.com/dog_rates/status/890240255349198849/photo/1	14	10	Cassie	doggo			doggo
10	890006608113172480	2017-07-26 00:31:25	Twitter for iPhone	This is Koda. He is a South Australian deckshark. Deceptively deadly. Frighteningly majestic. 13/10 would risk a petting #BarkWeek https://t.co/dVPW0B0Mme	https://twitter.com/dog_rates/status/890006608113172480/photo/1,https://twitter.com/dog_rates/status/890006608113172480/photo/1	13	10	Koda
11	889880896479866881	2017-07-25 16:11:53	Twitter for iPhone	This is Bruno. He is a service shark. Only gets out of the water to assist you. 13/10 terrifyingly good boy https://t.co/u1XPQMl29g	https://twitter.com/dog_rates/status/889880896479866881/photo/1	13	10	Bruno
12	889665388333682689	2017-07-25 01:55:32	Twitter for iPhone	Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm	https://twitter.com/dog_rates/status/889665388333682689/photo/1	13	10	NaN			puppo	puppo
13	889638837579907072	2017-07-25 00:10:02	Twitter for iPhone	This is Ted. He does his best. Sometimes that's not enough. But it's ok. 12/10 would assist https://t.co/f8dEDcrKSR	https://twitter.com/dog_rates/status/889638837579907072/photo/1,https://twitter.com/dog_rates/status/889638837579907072/photo/1	12	10	Ted
14	889531135344209921	2017-07-24 17:02:04	Twitter for iPhone	This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq	https://twitter.com/dog_rates/status/889531135344209921/photo/1	13	10	Stuart			puppo	puppo
15	889278841981685760	2017-07-24 00:19:32	Twitter for iPhone	This is Oliver. You're witnessing one of his many brutal attacks. Seems to be playing with his victim. 13/10 fr*ckin frightening #BarkWeek https://t.co/WpHvrQedPb	https://twitter.com/dog_rates/status/889278841981685760/video/1	13	10	Oliver
16	888917238123831296	2017-07-23 00:22:39	Twitter for iPhone	This is Jim. He found a fren. Taught him how to sit like the good boys. 12/10 for both https://t.co/chxruIOUJN	https://twitter.com/dog_rates/status/888917238123831296/photo/1	12	10	Jim
17	888804989199671297	2017-07-22 16:56:37	Twitter for iPhone	This is Zeke. He has a new stick. Very proud of it. Would like you to throw it for him without taking it. 13/10 would do my best https://t.co/HTQ77yNQ5K	https://twitter.com/dog_rates/status/888804989199671297/photo/1,https://twitter.com/dog_rates/status/888804989199671297/photo/1	13	10	Zeke
18	888554962724278272	2017-07-22 00:23:06	Twitter for iPhone	This is Ralphus. He's powering up. Attempting maximum borkdrive. 13/10 inspirational af https://t.co/YnYAFCTTiK	https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1	13	10	Ralphus
20	888078434458587136	2017-07-20 16:49:33	Twitter for iPhone	This is Gerald. He was just told he didn't get the job he interviewed for. A h*ckin injustice. 12/10 didn't want the job anyway https://t.co/DK7iDPfuRX	https://twitter.com/dog_rates/status/888078434458587136/photo/1,https://twitter.com/dog_rates/status/888078434458587136/photo/1	12	10	Gerald
21	887705289381826560	2017-07-19 16:06:48	Twitter for iPhone	This is Jeffrey. He has a monopoly on the pool noodles. Currently running a 'boop for two' midweek sale. 13/10 h*ckin strategic https://t.co/PhrUk20Q64	https://twitter.com/dog_rates/status/887705289381826560/photo/1	13	10	Jeffrey
22	887517139158093824	2017-07-19 03:39:09	Twitter for iPhone	I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba	https://twitter.com/dog_rates/status/887517139158093824/video/1	14	10	such
23	887473957103951883	2017-07-19 00:47:34	Twitter for iPhone	This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX	https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1	13	10	Canela
24	887343217045368832	2017-07-18 16:08:03	Twitter for iPhone	You may not have known you needed to see this today. 13/10 please enjoy (IG: emmylouroo) https://t.co/WZqNqygEyV	https://twitter.com/dog_rates/status/887343217045368832/video/1	13	10	NaN
25	887101392804085760	2017-07-18 00:07:08	Twitter for iPhone	This... is a Jubilant Antarctic House Bear. We only rate dogs. Please only send dogs. Thank you... 12/10 would suffocate in floof https://t.co/4Ad1jzJSdp	https://twitter.com/dog_rates/status/887101392804085760/photo/1	12	10	NaN
26	886983233522544640	2017-07-17 16:17:36	Twitter for iPhone	This is Maya. She's very shy. Rarely leaves her cup. 13/10 would find her an environment to thrive in https://t.co/I6oNy0CgiT	https://twitter.com/dog_rates/status/886983233522544640/photo/1,https://twitter.com/dog_rates/status/886983233522544640/photo/1	13	10	Maya
27	886736880519319552	2017-07-16 23:58:41	Twitter for iPhone	This is Mingus. He's a wonderful father to his smol pup. Confirmed 13/10, but he needs your help\n\nhttps://t.co/bVi0Yr4Cff https://t.co/ISvKOSkd5b	https://www.gofundme.com/mingusneedsus,https://twitter.com/dog_rates/status/886736880519319552/photo/1,https://twitter.com/dog_rates/status/886736880519319552/photo/1	13	10	Mingus
28	886680336477933568	2017-07-16 20:14:00	Twitter for iPhone	This is Derek. He's late for a dog meeting. 13/10 pet...al to the metal https://t.co/BCoWue0abA	https://twitter.com/dog_rates/status/886680336477933568/photo/1	13	10	Derek
29	886366144734445568	2017-07-15 23:25:31	Twitter for iPhone	This is Roscoe. Another pupper fallen victim to spontaneous tongue ejections. Get the BlepiPen immediate. 12/10 deep breaths Roscoe https://t.co/RGE08MIJox	https://twitter.com/dog_rates/status/886366144734445568/photo/1,https://twitter.com/dog_rates/status/886366144734445568/photo/1	12	10	Roscoe		pupper		pupper
31	886258384151887873	2017-07-15 16:17:19	Twitter for iPhone	This is Waffles. His doggles are pupside down. Unsure how to fix. 13/10 someone assist Waffles https://t.co/xZDA9Qsq1O	https://twitter.com/dog_rates/status/886258384151887873/photo/1	13	10	Waffles
33	885984800019947520	2017-07-14 22:10:11	Twitter for iPhone	Viewer discretion advised. This is Jimbo. He will rip ur finger right h*ckin off. Other dog clearly an accessory. 12/10 pls pet with caution https://t.co/BuveP0uMF1	https://twitter.com/dog_rates/status/885984800019947520/photo/1	12	10	Jimbo
34	885528943205470208	2017-07-13 15:58:47	Twitter for iPhone	This is Maisey. She fell asleep mid-excavation. Happens to the best of us. 13/10 would pat noggin approvingly https://t.co/tp1kQ8i9JF	https://twitter.com/dog_rates/status/885528943205470208/photo/1	13	10	Maisey
35	885518971528720385	2017-07-13 15:19:09	Twitter for iPhone	I have a new hero and his name is Howard. 14/10 https://t.co/gzLHboL7Sk	https://twitter.com/4bonds2carbon/status/885517367337512960	14	10	NaN
37	885167619883638784	2017-07-12 16:03:00	Twitter for iPhone	Here we have a corgi undercover as a malamute. Pawbably doing important investigative work. Zero control over tongue happenings. 13/10 https://t.co/44ItaMubBf	https://twitter.com/dog_rates/status/885167619883638784/photo/1,https://twitter.com/dog_rates/status/885167619883638784/photo/1,https://twitter.com/dog_rates/status/885167619883638784/photo/1,https://twitter.com/dog_rates/status/885167619883638784/photo/1	13	10	NaN
38	884925521741709313	2017-07-12 00:01:00	Twitter for iPhone	This is Earl. He found a hat. Nervous about what you think of it. 12/10 it's delightful, Earl https://t.co/MYJvdlNRVa	https://twitter.com/dog_rates/status/884925521741709313/photo/1	12	10	Earl
39	884876753390489601	2017-07-11 20:47:12	Twitter for iPhone	This is Lola. It's her first time outside. Must test the earth and taste the atmosphere. 13/10 you're doing great Lola https://t.co/74TKAUsLkO	https://twitter.com/dog_rates/status/884876753390489601/photo/1,https://twitter.com/dog_rates/status/884876753390489601/photo/1,https://twitter.com/dog_rates/status/884876753390489601/photo/1,https://twitter.com/dog_rates/status/884876753390489601/photo/1	13	10	Lola
40	884562892145688576	2017-07-11 00:00:02	Twitter for iPhone	This is Kevin. He's just so happy. 13/10 what is your secret Kevin https://t.co/1r4MFCbCX5	https://twitter.com/dog_rates/status/884562892145688576/photo/1	13	10	Kevin
41	884441805382717440	2017-07-10 15:58:53	Twitter for iPhone	I present to you, Pup in Hat. Pup in Hat is great for all occasions. Extremely versatile. Compact as h*ck. 14/10 (IG: itselizabethgales) https://t.co/vvBOcC2VdC	https://twitter.com/dog_rates/status/884441805382717440/photo/1	14	10	NaN
42	884247878851493888	2017-07-10 03:08:17	Twitter for iPhone	OMG HE DIDN'T MEAN TO HE WAS JUST TRYING A LITTLE BARKOUR HE'S SUPER SORRY 13/10 WOULD FORGIVE IMMEDIATE https://t.co/uF3pQ8Wubj	https://twitter.com/kaijohnson_19/status/883965650754039809	13	10	NaN
43	884162670584377345	2017-07-09 21:29:42	Twitter for iPhone	Meet Yogi. He doesn't have any important dog meetings today he just enjoys looking his best at all times. 12/10 for dangerously dapper doggo https://t.co/YSI00BzTBZ	https://twitter.com/dog_rates/status/884162670584377345/photo/1	12	10	Yogi	doggo			doggo